QoE-Driven Efficient Resource Utilisation for Video Over Critical Communication Systems

Author(s):  
Emad Abdullah Danish ◽  
Mazin I. Alshamrani

Research in network resource utilisation introduced several techniques for more efficient power and bandwidth consumption. The majority of these techniques, however, were based on Quality of Service (QoS) and network parameters. Therefore, in this study a different approach is taken to investigate the possibility of a more efficient resource utilisation if resources are distributed based on users' Quality of Experience (QoE), in the context of 3D video transmission over WiMAX access networks. In particular, this study suggests a QoE-driven technique to identify the operational regions (bounds) for Modulation and Coding Schemes (MCS). A mobile 3D video transmission is simulated, through which the correlation between receiver's Signal-to-Noise Ratio (SNR) and perceived video quality is identified. The main conclusions drawn from the study demonstrate that a considerable saving in signal power and bandwidth can be achieved in comparison to QoS-based techniques.

2012 ◽  
Vol 532-533 ◽  
pp. 1219-1224
Author(s):  
Hong Tao Deng

During video transmission over error prone network, compressed video bit-stream is sensitive to channel errors that may degrade the decoded pictures severely. In order to solve this problem, error concealment technique is a useful post-processing tool for recovering the lost information. In these methods, how to estimate the lost motion vector correctly is important for the quality of decoded picture. In order to recover the lost motion vector, an Decoder Motion Vector Estimation (DMVE) criterion was proposed and have well effect for recover the lost blocks. In this paper, we propose an improved error concealment method based on DMVE, which exploits the accurate motion vector by using redundant motion vector information. The experimental results with an H.264 codec show that our method improves both subjective and objective decoder reconstructed video quality, especially for sequences of drastic motion.


Author(s):  
Monalisa Ghosh ◽  
Chetna Singhal

Video streaming services top the internet traffic surging forward a competitive environment to impart best quality of experience (QoE) to the users. The standard codecs utilized in video transmission systems eliminate the spatiotemporal redundancies in order to decrease the bandwidth requirement. This may adversely affect the perceptual quality of videos. To rate a video quality both subjective and objective parameters can be used. So, it is essential to construct frameworks which will measure integrity of video just like humans. This chapter focuses on application of machine learning to evaluate the QoE without requiring human efforts with higher accuracy of 86% and 91% employing the linear and support vector regression respectively. Machine learning model is developed to forecast the subjective quality of H.264 videos obtained after streaming through wireless networks from the subjective scores.


Author(s):  
André F. Marquet ◽  
Jânio M. Monteiro ◽  
Nuno J. Martins ◽  
Mario S. Nunes

In legacy television services, user centric metrics have been used for more than twenty years to evaluate video quality. These subjective assessment metrics are usually obtained using a panel of human evaluators in standard defined methods to measure the impairments caused by a diversity of factors of the Human Visual System (HVS), constituting what is also called Quality of Experience (QoE) metrics. As video services move to IP networks, the supporting distribution platforms and the type of receiving terminals is getting more heterogeneous, when compared with classical video distributions. The flexibility introduced by these new architectures is, at the same time, enabling an increment of the transmitted video quality to higher definitions and is supporting the transmission of video to lower capability terminals, like mobile terminals. In IP Networks, while Quality of Service (QoS) metrics have been consistently used for evaluating the quality of a transmission and provide an objective way to measure the reliability of communication networks for various purposes, QoE metrics are emerging as a solution to address the limitations of conventional QoS measuring when evaluating quality from the service and user point of view. In terms of media, compressed video usually constitutes a very interdependent structure degrading in a non-graceful manner when exposed to Binary Erasure Channels (BEC), like the Internet or wireless networks. Accordingly, not only the type of encoder and its major encoding parameters (e.g. transmission rate, image definition or frame rate) contribute to the quality of a received video, but also QoS parameters are usually a cause for different types of decoding artifacts. As a result of this, several worldwide standard entities have been evaluating new metrics for the subjective assessment of video transmission over IP networks. In this chapter we are especially interested in explaining some of the best practices available to monitor, evaluate and assure good levels of QoE in packet oriented networks for rich media applications like high quality video streaming. For such applications, service requirements are relatively loose or difficult to quantify and therefore specific techniques have to be clearly understood and evaluated. By the mid of the chapter the reader should have understood why even networks with excellent QoS parameters might have QoE issues, as QoE is a systemic approach that does not relate solely to QoS but to the ensemble of components composing the communication system.


Author(s):  
Ashraf M.A. Ahmad

Video streaming poses significant technical challenges in quality of service guarantee and efficient resource management. Generally, it is recognized that end-to-end quality requirements of video streaming application can be reasonably achieved only by integrative study of advanced networking and content processing techniques. However, most existing integration techniques stop at the bit stream level, ignoring a deeper understanding of the media content. Yet, the underlying visual content of the video stream contains a vast amount of information that can be used to predict the bit-rate or quality more accurately. In the content-aware video streaming framework, video content is extracted automatically and used to control video quality under various manipulations and network resource requirements.


1970 ◽  
Vol 108 (2) ◽  
pp. 27-30 ◽  
Author(s):  
S. Paulikas ◽  
P. Sargautis ◽  
V. Banevicius

The problem of estimation of video quality obtained by end-user for mobile video streaming is addressed. Widely spreading mobile communication systems and increasing data transmission rates expand variety of multimedia services. One of such services is video streaming. So it is important to assess quality of this service. Consumers of video streaming are humans, and quality assessment must account human perception characteristics. Existing methods for user experienced video quality estimation as quality metrics usually usebit-error rate that has low correlation with by human perceived video quality. More advanced methods usually require too much processing power that cannot be obtained in handled mobile devices or intrusion into device firmware and/or hardware to obtain required data. However, recent research shows that channels throughput dedicated to some service (e.g. video streaming) can be tied to QoS perceived by an end-user indicator. This paper presents a research on impact of wireless channel parameters such as throughput and jitter on quality of video streaming. These wireless channel parameters can be easily obtained by monitoring IP level data streams in end-user’s device by fairly simple software agent for indication of video streaming QoS. Ill. 5, bibl. 10 (in English; abstracts in English and Lithuanian).http://dx.doi.org/10.5755/j01.eee.108.2.138


2013 ◽  
Author(s):  
Dragana S Djordjevic ◽  
David Okolisan ◽  
Maja Pokric ◽  
Dragan Kukolj

Author(s):  
Anthony Olufemi Tesimi Adeyemi-Ejeye ◽  
Geza Koczian ◽  
Mohammed Abdulrahman Alreshoodi ◽  
Michael C. Parker ◽  
Stuart D. Walker

With the standardization of ultra-high-definition formats and their increasing adoption within the multimedia industry, it has become vital to investigate how such a resolution could impact the quality of experience with respect to mission-critical communication systems. While this standardization enables improved perceptual quality of video content, how it can be used in mission-critical communications remains a challenge, with the main challenge being processing. This chapter discusses the challenges and potential solutions for the deployment of ultra-high-definition video transmission for mission-critical applications. In addition, it examines the state-of-the-art solutions for video processing and explores potential solutions. Finally, the authors predict future research directions in this area.


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